High-resolution unmanned aerial vehicle SAR sparse imaging method based on frequency band synthesis

Abstract

The invention provides a high-resolution unmanned aerial vehicle SAR sparse imaging method based on frequency band synthesis. The method comprises the steps that a first matrix of multiple sets of radar echo data is acquired; performing sparse sampling on the first matrix of each group of radar echo data to obtain a second matrix corresponding to the first matrix; based on the matrix, calculating to obtain a third matrix of each group of radar echo data; performing range migration correction on the third matrix of each group of radar echo data to obtain first target data of each group of radar echo data; performing bandwidth synthesis on the first target data of each group of radar echo data to obtain second target data; constructing an approximate observation operator based on the first matrix of each group of radar echo data, and imaging the second target data based on the approximate observation operator to obtain a pre-output image; and carrying out multiple times of residual phase error correction on the pre-output image to obtain an imaging image. The precision of the image in the reconstruction scene can be improved.

Description

Technical Field

[0001] The present application belongs to the field of radar imaging technology, and in particular to a high-resolution unmanned aerial vehicle SAR sparse imaging method with frequency band synthesis. Background Art

[0002] Synthetic Aperture Radar (SAR) is a microwave imaging radar with two-dimensional high resolution. It achieves high resolution in the range direction through pulse compression technology, and high resolution in the azimuth direction through synthetic aperture technology. With the continuous growth of application demand, the requirements for SAR image resolution are getting higher and higher. In order to obtain high-resolution SAR images, radar signals with a larger bandwidth must be emitted, and the receiving end needs to have a higher analog to digital converter (ADC) sampling rate. This undoubtedly poses a huge challenge to the instantaneous bandwidth of data signal processing.

[0003] In order to achieve SAR high-resolution imaging without increasing the system hardware requirements, Stepped Frequency Chirp Synthetic Aperture Radar (SFCSAR) came into being. SFCSAR can achieve high-resolution imaging of the observed scene without increasing the radar instantaneous bandwidth and the front-end ADC sampling rate. Its working principle is to transmit multiple narrow-band sub-pulse signals at the transmitter, and synthesize these narrow-band sub-pulse signals into a large-bandwidth signal at the receiver through digital signal processing technology, and then use the signal to achieve high-resolution imaging of the scene. However, to obtain SAR images with the same high resolution, the amount of data required to be processed has not decreased, which poses a huge challenge to the algorithm's operating efficiency and system memory. How to achieve SAR high-resolution imaging under the constraints of existing hardware conditions has become a research hotspot for many scholars. In recent years, with the widespread application of new small and medium-sized platforms such as drones, the research on airborne SAR systems has received widespread attention. Compared with spaceborne SAR systems, airborne SAR systems are more flexible. However, since the carrier aircraft is easily affected by various uncertain factors in the air, the motion trajectory deviates from the ideal track. The motion errors caused by these non-ideal motions have a huge impact on the radar echo phase. If the errors are not compensated, the SAR imaging quality will be seriously affected.

[0004] Compared with traditional matched filter algorithms, the compressed sensing theory radar imaging method can achieve sparse reconstruction of the observation scene while greatly reducing the amount of data required. However, for SAR systems such as SFCSAR, where the amount of sampling data in the range and azimuth directions is much larger than that of conventional signal systems, the use of traditional precise observation operators will lead to huge computational workload and memory usage problems, which have not been fundamentally solved. Based on this, people have found that the use of approximate observation operators can alleviate the problems of computational workload and memory usage, but the result is that the phase error range that the approximate observation operator can correct is limited, which seriously affects the quality of the reconstructed image. Summary of the invention

[0005] The embodiments of the present application provide a high-resolution unmanned aerial vehicle SAR sparse imaging method, device, equipment and medium with frequency band synthesis to solve the problem that the phase error range of the approximate observation operator correction is limited, which seriously affects the quality of the reconstructed image and leads to low image accuracy of the existing reconstructed scene.

[0006] This application is implemented through the following technical solutions:

[0007] In a first aspect, an embodiment of the present application provides a high-resolution unmanned aerial vehicle SAR sparse imaging method with frequency band synthesis, comprising:

[0008] A first matrix of multiple groups of radar echo data is obtained; wherein the first matrix is ​​a full sampling matrix, and the frequency of each group of radar echo data is different.

[0009] Sparse sampling is performed on the first matrix of each group of radar echo data to obtain a second matrix corresponding to the first matrix.

[0010] Based on the first matrix of each set of radar echo data and the second matrix corresponding to the first matrix, a third matrix of each set of radar echo data is calculated.

[0011] The third matrix of each group of radar echo data is respectively subjected to range migration correction to obtain the first target data of each group of radar echo data.

[0012] Bandwidth synthesis is performed on the first target data of each group of radar echo data to obtain second target data.

[0013] Based on the first matrix of each group of radar echo data, an approximate observation operator is constructed, and the second target data is imaged based on the approximate observation operator to obtain a pre-output image.

[0014] The residual phase error of the pre-output image is corrected multiple times to obtain an imaging image.

[0015] In combination with the first aspect, in some possible implementations, constructing an approximate observation operator based on a first matrix of each set of radar echo data includes:

[0016] The first matrix of each group of radar echo data is processed based on the clutter locking method to obtain the Doppler center frequency of each group of radar echo data; the first matrix of each group of radar echo data is processed based on the PGA algorithm to obtain the first error of each group of radar echo data; wherein the first error includes an azimuth phase error and a range migration error.

[0017] An approximate observation operator is constructed based on the Doppler center frequency of each set of radar echo data and the first error of each set of radar echo data.

[0018] In combination with the first aspect, in some possible implementations, constructing an approximate observation operator based on the Doppler center frequency of each set of radar echo data and the first error of each set of radar echo data includes:

[0019] Using the first formula, construct an approximate observation operator;

[0020] The first formula is:

[0021]

[0022] Among them, Γ -1 (X) represents the approximate observation operator of image X, represents the azimuth compression factor, φ1 represents the mean azimuth phase error of each set of radar echo data, represents the range migration correction factor, φ0 represents the mean value of the range migration error of each group of radar echo data, and F r Indicates the distance to FFT transformation, represents the azimuth inverse FFT transform, f c represents the center frequency of the linear frequency modulation signal obtained after the narrowband sub-pulse synthesis, f d Represents the mean value of the Doppler center frequency of each group of radar echo data, f r represents the distance frequency, R represents the distance between the radar and the target, λ represents the wavelength, v represents the wave speed, t represents the time, Φ X represents the residual phase error after image X is focused.

[0023] In combination with the first aspect, in some possible implementations, bandwidth synthesis is performed on the first target data of each group of radar echo data to obtain the second target data, including:

[0024] The first target data of each group of radar echo data is bandwidth synthesized based on a frequency domain bandwidth synthesis algorithm to obtain second target data.

[0025] In combination with the first aspect, in some possible implementations, imaging the second target data based on the approximate observation operator to obtain a pre-output image includes:

[0026] The second target data is imaged using the second formula and an approximate observation operator to obtain a pre-output image;

[0027] The second formula is:

[0028] G=Γ(E)=Fa(Φ G ·Θ(E))=Fa(Φ G ·Fr H (E·Hc)·Ha)

[0029] Where G represents the pre-output image, E = Γ -1 (G), E represents the second target data, Θ(E) = F r H (E.H c )·H a , Fa represents the azimuth FFT transform, F H

[0030] r Indicates the distance to inverse FFT transform, Φ G represents the residual phase error of the pre-output image G after focusing.

[0031] In combination with the first aspect, in some possible implementations, performing multiple residual phase error corrections on the pre-output image to obtain an imaging image includes:

[0032] Using the third formula, multiple residual phase error corrections are performed on the pre-output image;

[0033] The third formula is:

[0034]

[0035] Among them, G i+1 represents the image, the image after i+1 residual phase error correction, E represents the second target data, L represents the sparse sampling operator, G represents the pre-output image, Φ i+1 represents the residual phase error during the ith residual phase error correction, ζ represents the regularization coefficient, represents the azimuth compression factor, φ1 represents the mean azimuth phase error of each set of radar echo data, represents the range migration correction factor, φ0 represents the mean value of the range migration error of each group of radar echo data, and F r Indicates the distance to FFT transformation, f c represents the center frequency of the linear frequency modulation signal obtained after the narrowband sub-pulse synthesis, f d Represents the mean value of the Doppler center frequency of each group of radar echo data, f rrepresents the distance frequency, R represents the distance between the radar and the target, λ represents the wavelength, v represents the wave speed, and t represents the time.

[0036] In combination with the first aspect, in some possible implementations, based on the first matrix of each set of radar echo data and the second matrix corresponding to the first matrix, a third matrix of each set of radar echo data is calculated, including:

[0037] The sum of the first matrix of each group of radar echo data and its corresponding second matrix is ​​calculated respectively to obtain the third matrix of each group of radar echo data.

[0038] In a second aspect, an embodiment of the present application provides a high-resolution unmanned aerial vehicle SAR sparse imaging device with frequency band synthesis, comprising:

[0039] The data acquisition module is used to acquire a first matrix of multiple groups of radar echo data; wherein the first matrix is ​​a full sampling matrix, and the frequency of each group of radar echo data is different.

[0040] The first calculation module is used to perform sparse sampling on the first matrix of each group of radar echo data to obtain a second matrix corresponding to the first matrix.

[0041] The second calculation module is used to calculate the third matrix of each group of radar echo data based on the first matrix of each group of radar echo data and the second matrix corresponding to the first matrix.

[0042] The third calculation module is used to perform range migration correction on the third matrix of each group of radar echo data to obtain the first target data of each group of radar echo data.

[0043] The first synthesis module is used to perform bandwidth synthesis on the first target data of each group of radar echo data to obtain second target data.

[0044] The first correction module is used to construct an approximate observation operator based on the first matrix of each group of radar echo data, and to image the second target data based on the approximate observation operator to obtain a pre-output image.

[0045] The result imaging module is used to perform multiple residual phase error corrections on the pre-output image to obtain an imaging image.

[0046] In a third aspect, an embodiment of the present application provides a terminal device, comprising: a processor and a memory, the memory being used to store a computer program, and when the processor executes the computer program, the high-resolution unmanned aerial vehicle SAR sparse imaging method with frequency band synthesis as described in any one of the first aspects is implemented.

[0047] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the high-resolution unmanned aerial vehicle SAR sparse imaging method with frequency band synthesis as described in any one of the first aspects is implemented.

[0048] It can be understood that the beneficial effects of the second to fourth aspects mentioned above can be found in the relevant description of the first aspect mentioned above, and will not be repeated here.

[0049] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0050] The present application first calculates an approximate observation operator based on a first matrix of multiple groups of radar echo data. Subsequently, sparse sampling is performed on the first matrix to generate a second matrix. Next, multiple third matrices are generated using the first matrix and the second matrix, and range migration correction and bandwidth synthesis are performed on these third matrices to obtain second target data. Afterwards, the second target data is imaged using the previously constructed approximate observation operator to obtain a pre-output image. Finally, the final imaging image is obtained by performing multiple residual phase error corrections on the pre-output image. The approximate observation operator of the present application is obtained based on the calculation of the azimuth phase error, the range migration error, and the Doppler center frequency, which expands the range of phase errors that the approximate observation operator can correct. Therefore, when the second target data is imaged using the approximate observation operator, the accuracy of the pre-output image can be improved, thereby ultimately enhancing the quality and accuracy of the imaging.

[0051] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present specification. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0053] Figure 1 It is a flow chart of a high-resolution unmanned aerial vehicle SAR sparse imaging method of frequency band synthesis provided in one embodiment of the present application;

[0054] Figure 2 This is a schematic diagram of the structure of a high-resolution unmanned aerial vehicle SAR sparse imaging device with frequency band synthesis provided in one embodiment of the present application;

[0055] Figure 3 It is a structural diagram of a terminal device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0056] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0057] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, wholes, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or combinations thereof.

[0058] It should also be understood that the term “and / or” used in the specification and appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0059] As used in the specification and appended claims of this application, the term "if" can be interpreted as "when" or "uponce" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" can be interpreted as meaning "uponce it is determined" or "in response to determining" or "uponce [described condition or event] is detected" or "in response to detecting [described condition or event]", depending on the context.

[0060] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0061] References to "one embodiment" or "some embodiments" etc. described in the specification of this application mean that one or more embodiments of the present application include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0062] The present application provides a high-resolution unmanned aerial vehicle SAR sparse imaging method with frequency band synthesis. Figure 1 is a schematic diagram of the process of a high-resolution unmanned aerial vehicle SAR sparse imaging method for frequency band synthesis provided in an embodiment of the present application, with reference to Figure 1 The detailed description of the high-resolution UAV-borne SAR sparse imaging method for this frequency band synthesis is as follows:

[0063] Step 101 , obtaining a first matrix of multiple groups of radar echo data; wherein the first matrix is ​​a full sampling matrix, and the frequency of each group of radar echo data is different.

[0064] Step 102: sparsely sample the first matrix of each group of radar echo data to obtain a second matrix corresponding to the first matrix.

[0065] Step 103 : Based on the first matrix of each set of radar echo data and the second matrix corresponding to the first matrix, a third matrix of each set of radar echo data is calculated.

[0066] Exemplarily, step 103 may include:

[0067] The sum of the first matrix of each group of radar echo data and its corresponding second matrix is ​​calculated respectively to obtain the third matrix of each group of radar echo data.

[0068] Step 104 , performing range migration correction on the third matrix of each set of radar echo data to obtain first target data of each set of radar echo data.

[0069] Exemplarily, the specific process of performing range migration correction on the third matrix of each set of radar echo data includes:

[0070] For each third matrix, the range migration error of the third matrix is ​​obtained; based on the range migration error, a phase compensation factor is constructed; and the product of the radar echo data corresponding to the third matrix and the phase compensation factor is calculated to obtain the first target data corresponding to the third matrix.

[0071] Step 105 , performing bandwidth synthesis on the first target data of each group of radar echo data to obtain second target data.

[0072] Exemplarily, performing bandwidth synthesis on the first target data of each group of radar echo data to obtain the second target data includes:

[0073] Based on the frequency domain bandwidth synthesis algorithm (i.e., the algorithm of first distance compression and then synthesis), the first target data of each group of radar echo data is subjected to bandwidth synthesis to obtain the second target data.

[0074] Step 106: construct an approximate observation operator based on the first matrix of each group of radar echo data, and image the second target data based on the approximate observation operator to obtain a pre-output image.

[0075] Exemplarily, the calculating the approximate observation operator based on the first matrix of each set of radar echo data may include:

[0076] The first matrix of each group of radar echo data is processed based on the clutter locking method to obtain the Doppler center frequency of each group of radar echo data; the first matrix of each group of radar echo data is processed based on the PGA algorithm to obtain the first error of each group of radar echo data; wherein the first error includes an azimuth phase error and a range migration error.

[0077] An approximate observation operator is constructed based on the Doppler center frequency of each set of radar echo data and the first error of each set of radar echo data.

[0078] Exemplarily, the first error also includes a residual phase error. The PGA algorithm can also estimate the phase error (residual phase error) of a single sub-pulse echo data. This phase error can be used as the phase error of the synthetic bandwidth echo data (a set of radar echo data) because the difference between the phase error estimated for a single sub-pulse and the phase error estimated for the full-bandwidth pulse is very small.

[0079] For example, because the approximate observation operator is calculated based on the azimuth phase error, range migration error and Doppler center frequency, when the approximate observation operator is used to reconstruct the second target data, the data accuracy of the pre-output image can be improved, ultimately improving the quality and accuracy of imaging.

[0080] Exemplarily, constructing an approximate observation operator based on the Doppler center frequency of each set of radar echo data and the first error of each set of radar echo data may include:

[0081] Using the first formula, construct an approximate observation operator;

[0082] The first formula is:

[0083]

[0084] Among them, Γ -1 (X) represents the approximate observation operator of image X, represents the azimuth compression factor, φ1 represents the mean azimuth phase error of each set of radar echo data, represents the range migration correction factor, φ0 represents the mean value of the range migration error of each group of radar echo data, and F r Indicates the distance to FFT transformation, represents the azimuth inverse FFT transform, f c represents the center frequency of the linear frequency modulation signal obtained after the narrowband sub-pulse synthesis, f d Represents the mean value of the Doppler center frequency of each group of radar echo data, f r represents the distance frequency, R represents the distance between the radar and the target, λ represents the wavelength, v represents the wave speed, t represents the time, Φ X represents the residual phase error after image X is focused.

[0085] Exemplarily, imaging the second target data based on an approximate observation operator to obtain a pre-output image includes:

[0086] The second target data is imaged using the second formula and an approximate observation operator to obtain a pre-output image;

[0087] The second formula is:

[0088] G=Γ(E)=Fa(Φ G ·Θ(E))=Fa(Φ G ·Fr H (E·Hc)·Ha)

[0089] Where G represents the pre-output image, E = Γ -1 (G), E represents the second target data, Θ(E) = F r H (E.H c )·H a , Fa represents the azimuth FFT transform, F H

[0090] r Indicates the distance to inverse FFT transform, Φ G represents the residual phase error of the pre-output image G after focusing.

[0091] For example, compared with the traditional method of using precise observation operators to correct images, the approximate observation operator of the present application can greatly improve the efficiency of correction and reduce the occupation of computer memory when the algorithm is running. At the same time, in the present application, the calculation of the approximate observation operator is improved by combining the Doppler center frequency. This improvement expands the range of phase errors that can be corrected by the approximate observation operator, thereby ensuring the accuracy of scene reconstruction.

[0092] Step 107, performing multiple residual phase error corrections on the pre-output image to obtain an imaging image.

[0093] Exemplarily, step 107 may include:

[0094] Using the third formula, multiple residual phase error corrections are performed on the pre-output image;

[0095] The third formula is:

[0096]

[0097] Among them, G i+1 represents the image, the image after i+1 residual phase error correction, E represents the second target data, L represents the sparse sampling operator, G represents the pre-output image, Φ i+1 represents the residual phase error during the ith residual phase error correction, ζ represents the regularization coefficient, represents the azimuth compression factor, φ1 represents the mean azimuth phase error of each set of radar echo data, represents the range migration correction factor, φ0 represents the mean value of the range migration error of each group of radar echo data, and F r Indicates the distance to FFT transformation, f c represents the center frequency of the linear frequency modulation signal obtained after the narrowband sub-pulse synthesis, f d Represents the mean value of the Doppler center frequency of each group of radar echo data, f r represents the distance frequency, R represents the distance between the radar and the target, λ represents the wavelength, v represents the wave speed, and t represents the time.

[0098] Exemplarily, step 107 of the present application utilizes the idea of ​​multiple-loop correction, which can further reduce image errors. Multiple-loop correction can effectively improve the reconstruction accuracy of the scene and improve computing efficiency.

[0099] The above-mentioned high-resolution unmanned aerial vehicle SAR sparse imaging method with frequency band synthesis first calculates an approximate observation operator based on a first matrix of multiple groups of radar echo data. Subsequently, sparse sampling is performed on this first matrix to generate a second matrix. Next, multiple third matrices are generated using the first matrix and the second matrix, and range migration correction and bandwidth synthesis are performed on these third matrices to obtain second target data. Afterwards, the second target data is imaged using the previously constructed approximate observation operator to obtain a pre-output image. Finally, the final imaging image is obtained by performing multiple residual phase error corrections on the pre-output image. The approximate observation operator of the present application is obtained based on the calculation of azimuth phase error, range migration error and Doppler center frequency, which expands the phase error range that the approximate observation operator can correct. Therefore, when the second target data is imaged using the approximate observation operator, the accuracy of the pre-output image can be improved, thereby ultimately enhancing the quality and accuracy of imaging.

[0100] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0101] Corresponding to the high-resolution unmanned aerial vehicle SAR sparse imaging method with frequency band synthesis described in the above embodiment, Figure 2 A structural block diagram of a high-resolution unmanned aerial vehicle SAR sparse imaging device with frequency band synthesis provided in an embodiment of the present application is shown. For ease of explanation, only the parts related to the embodiment of the present application are shown.

[0102] See also Figure 2 The high-resolution unmanned aerial vehicle SAR sparse imaging device with frequency band synthesis in the embodiment of the present application may include:

[0103] The data acquisition module 201 is used to acquire a first matrix of multiple groups of radar echo data; wherein the first matrix is ​​a full sampling matrix, and the frequency of each group of radar echo data is different.

[0104] The first calculation module 202 is used to perform sparse sampling on the first matrix of each group of radar echo data to obtain a second matrix corresponding to the first matrix.

[0105] The second calculation module 203 is used to calculate and obtain a third matrix of each group of radar echo data based on the first matrix of each group of radar echo data and a second matrix corresponding to the first matrix.

[0106] The third calculation module 204 is used to perform range migration correction on the third matrix of each group of radar echo data to obtain the first target data of each group of radar echo data.

[0107] The first synthesis module 205 is used to perform bandwidth synthesis on the first target data of each group of radar echo data to obtain second target data.

[0108] The first correction module 206 is used to construct an approximate observation operator based on the first matrix of each group of radar echo data, and to image the second target data based on the approximate observation operator to obtain a pre-output image.

[0109] The result imaging module 207 is used to perform multiple residual phase error corrections on the pre-output image to obtain an imaging image.

[0110] Exemplarily, the first correction module 206 may be used to:

[0111] The first matrix of each group of radar echo data is processed based on the clutter locking method to obtain the Doppler center frequency of each group of radar echo data; the first matrix of each group of radar echo data is processed based on the PGA algorithm to obtain the first error of each group of radar echo data; wherein the first error includes an azimuth phase error and a range migration error.

[0112] An approximate observation operator is constructed based on the Doppler center frequency of each set of radar echo data and the first error of each set of radar echo data.

[0113] Exemplarily, the first correction module 206 may be used to:

[0114] Using the first formula, construct an approximate observation operator;

[0115] The first formula is:

[0116]

[0117] Among them, Γ -1 (X) represents the approximate observation operator of image X, represents the azimuth compression factor, φ1 represents the mean azimuth phase error of each set of radar echo data, represents the range migration correction factor, φ0 represents the mean value of the range migration error of each group of radar echo data, and F r Indicates the distance to FFT transformation, represents the azimuth inverse FFT transform, f c represents the center frequency of the linear frequency modulation signal obtained after the narrowband sub-pulse synthesis, f d Represents the mean value of the Doppler center frequency of each group of radar echo data, f r represents the distance frequency, R represents the distance between the radar and the target, λ represents the wavelength, v represents the wave speed, t represents the time, Φ X represents the residual phase error after image X is focused.

[0118] Exemplarily, the first synthesis module 205 may be used to:

[0119] The first target data of each group of radar echo data is bandwidth synthesized based on a frequency domain bandwidth synthesis algorithm to obtain second target data.

[0120] Exemplarily, the first correction module 206 may be used to:

[0121] The second target data is imaged using the second formula and an approximate observation operator to obtain a pre-output image;

[0122] The second formula is:

[0123] G=Γ(E)=Fa(Φ G ·Θ(E))=Fa(Φ G ·Fr H (E·Hc)·Ha)

[0124] Where G represents the pre-output image, E = Γ -1 (G), E represents the second target data, Θ(E) = Fr H (E.H c )·H a , Fa represents the azimuth FFT transform, Fr H Indicates the distance to inverse FFT transform, Φ G represents the residual phase error of the pre-output image G after focusing.

[0125] Exemplarily, the result imaging module 207 may be used to:

[0126] Using the third formula, multiple residual phase error corrections are performed on the pre-output image;

[0127] The third formula is:

[0128]

[0129] Among them, G i+1 represents the image, the image after i+1 residual phase error correction, E represents the second target data, L represents the sparse sampling operator, G represents the pre-output image, Φ i+1 represents the residual phase error during the ith residual phase error correction, ζ represents the regularization coefficient, represents the azimuth compression factor, φ1 represents the mean azimuth phase error of each set of radar echo data, represents the range migration correction factor, φ0 represents the mean value of the range migration error of each group of radar echo data, and F r Indicates the distance to FFT transformation, f c represents the center frequency of the linear frequency modulation signal obtained after the narrowband sub-pulse synthesis, f d Represents the mean value of the Doppler center frequency of each group of radar echo data, f r represents the distance frequency, R represents the distance between the radar and the target, λ represents the wavelength, v represents the wave speed, and t represents the time.

[0130] Exemplarily, the second calculation module 203 may be used for:

[0131] The sum of the first matrix of each group of radar echo data and its corresponding second matrix is ​​calculated respectively to obtain the third matrix of each group of radar echo data.

[0132] It should be noted that the information interaction, execution process, etc. between the above-mentioned devices / units are based on the same concept as the method embodiment of the present application. Their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.

[0133] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0134] The present application also provides a terminal device, see Figure 3 The terminal device 300 may include: at least one processor 310 and a memory 320, wherein the memory 320 is used to store a computer program 321, and the processor 310 is used to call and run the computer program 321 stored in the memory 320 to implement the steps in any of the above-mentioned method embodiments, for example Figure 1 Steps 101 to 107 in the illustrated embodiment. Alternatively, when the processor 310 executes the computer program, the functions of each module / unit in the above-mentioned device embodiments are implemented, for example Figure 2 The functions of each module are shown.

[0135] Exemplarily, the computer program 321 may be divided into one or more modules / units, one or more modules / units are stored in the memory 320, and are executed by the processor 310 to complete the present application. The one or more modules / units may be a series of computer program segments that can complete specific functions, and the program segments are used to describe the execution process of the computer program in the terminal device 300.

[0136] Those skilled in the art will understand that Figure 3 It is only an example of a terminal device and does not constitute a limitation on the terminal device. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components, such as input and output devices, network access devices, buses, etc.

[0137] The processor 310 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor may be any conventional processor, etc.

[0138] The memory 320 may be an internal storage unit of the terminal device, or an external storage device of the terminal device, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. The memory 320 is used to store the computer program and other programs and data required by the terminal device. The memory 320 may also be used to temporarily store data that has been output or is to be output.

[0139] The bus may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, the bus in the drawings of the present application is not limited to only one bus or one type of bus.

[0140] The high-resolution unmanned aerial vehicle SAR sparse imaging method with frequency band synthesis provided in the embodiments of the present application can be applied to terminal devices such as computers, wearable devices, vehicle-mounted devices, tablet computers, laptops, netbooks, etc. The embodiments of the present application do not impose any restrictions on the specific types of terminal devices.

[0141] An embodiment of the present application further provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps in each embodiment of the high-resolution unmanned aerial vehicle SAR sparse imaging method that can realize the above-mentioned frequency band synthesis.

[0142] An embodiment of the present application provides a computer program product. When the computer program product runs on a mobile terminal, the mobile terminal implements the steps in each embodiment of the high-resolution unmanned aerial vehicle SAR sparse imaging method that can realize the above-mentioned frequency band synthesis.

[0143] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, which can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may at least include: any entity or device that can carry the computer program code to the camera device / terminal device, recording medium, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electrical carrier signal, telecommunication signal and software distribution medium. For example, a USB flash drive, a mobile hard disk, a disk or an optical disk.

[0144] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0145] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0146] In the embodiments provided in the present application, it should be understood that the disclosed devices / network equipment and methods can be implemented in other ways. For example, the device / network equipment embodiments described above are merely schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0147] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0148] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A high-resolution unmanned aerial vehicle SAR sparse imaging method with frequency band synthesis, characterized in that: include: Acquire a first matrix of multiple groups of radar echo data; wherein the first matrix is ​​a full sampling matrix, and the frequency of each group of radar echo data is different; Sparsely sampling the first matrix of each set of radar echo data to obtain a second matrix corresponding to the first matrix; Based on the first matrix of each set of radar echo data and the second matrix corresponding to the first matrix, a third matrix of each set of radar echo data is calculated; Performing range migration correction on the third matrix of each group of radar echo data respectively to obtain first target data of each group of radar echo data; Performing bandwidth synthesis on the first target data of each group of radar echo data to obtain second target data; Based on the first matrix of each group of radar echo data, an approximate observation operator is constructed, and the second target data is imaged based on the approximate observation operator to obtain a pre-output image; The pre-output image is subjected to multiple residual phase error corrections to obtain an imaging image.

2. The high-resolution unmanned aerial vehicle SAR sparse imaging method with frequency band synthesis as claimed in claim 1, characterized in that: The constructing of an approximate observation operator based on the first matrix of each set of radar echo data comprises: The first matrix of each group of radar echo data is processed based on the clutter locking method to obtain the Doppler center frequency of each group of radar echo data; the first matrix of each group of radar echo data is processed based on the PGA algorithm to obtain the first error of each group of radar echo data; wherein the first error includes an azimuth phase error and a range migration error; An approximate observation operator is constructed based on the Doppler center frequency of each set of radar echo data and the first error of each set of radar echo data.

3. The high-resolution unmanned aerial vehicle SAR sparse imaging method with frequency band synthesis as claimed in claim 2, characterized in that: The constructing of an approximate observation operator based on the Doppler center frequency of each group of radar echo data and the first error of each group of radar echo data comprises: Adopting the first formula to construct the approximate observation operator; The first formula is: C -1 (X)=Θ -1 (F X F a H (X)) Among them, Γ -1 (X) represents the approximate observation operator of image X, represents the azimuth compression factor, φ1 represents the mean azimuth phase error of each set of radar echo data, represents the range migration correction factor, φ0 represents the mean value of the range migration error of each group of radar echo data, and F r Indicates the distance to FFT transformation, F a H represents the azimuth inverse FFT transform, f c represents the center frequency of the linear frequency modulation signal obtained after the narrowband sub-pulse synthesis, f d Represents the mean value of the Doppler center frequency of each group of radar echo data, f r represents the distance frequency, R represents the distance between the radar and the target, λ represents the wavelength, v represents the wave speed, t represents the time, Φ X represents the residual phase error after image X is focused.

4. The high-resolution unmanned aerial vehicle SAR sparse imaging method with frequency band synthesis as claimed in claim 1, characterized in that: The step of performing bandwidth synthesis on the first target data of each group of radar echo data to obtain second target data includes: Bandwidth synthesis is performed on the first target data of each group of radar echo data based on a frequency domain bandwidth synthesis algorithm to obtain the second target data.

5. The high-resolution unmanned aerial vehicle SAR sparse imaging method with frequency band synthesis as claimed in claim 3, characterized in that: The imaging of the second target data based on the approximate observation operator to obtain a pre-output image includes: Adopting a second formula and using the approximate observation operator to image the second target data to obtain the pre-output image; The second formula is: G=Γ(E)=Fa(Φ G ·Θ(E))=Fa(Φ G ·Fr H (E·Hc)·Ha) Wherein, G represents the pre-output image, E=Γ -1 (G), E represents the second target data, Θ(E) = F r H (E.H c )·H a , F a Indicates the azimuth FFT transform, F r H Indicates the distance to inverse FFT transform, Φ G represents the residual phase error of the pre-output image G after focusing.

6. The high-resolution unmanned aerial vehicle SAR sparse imaging method with frequency band synthesis as claimed in claim 1, characterized in that: The performing multiple residual phase error corrections on the pre-output image to obtain an imaging image comprises: Using the third formula, performing multiple residual phase error corrections on the pre-output image; The third formula is: Among them, G i+1 represents the image, i.e., the image after i+1 residual phase error correction, E represents the second target data, L represents the sparse sampling operator, G represents the pre-output image, Φ i+1 represents the residual phase error during the ith residual phase error correction, ζ represents the regularization coefficient, represents the azimuth compression factor, φ1 represents the mean azimuth phase error of each set of radar echo data, represents the range migration correction factor, φ0 represents the mean value of the range migration error of each group of radar echo data, and F r Indicates the distance to FFT transformation, f c represents the center frequency of the linear frequency modulation signal obtained after the narrowband sub-pulse synthesis, f d Represents the mean value of the Doppler center frequency of each group of radar echo data, f r represents the distance frequency, R represents the distance between the radar and the target, λ represents the wavelength, v represents the wave speed, and t represents the time.

7. The high-resolution unmanned aerial vehicle SAR sparse imaging method with frequency band synthesis as claimed in claim 1, characterized in that: The step of calculating the third matrix of each group of radar echo data based on the first matrix of each group of radar echo data and the second matrix corresponding to the first matrix comprises: The sum of the first matrix of each group of radar echo data and its corresponding second matrix is ​​calculated respectively to obtain the third matrix of each group of radar echo data.

8. A high-resolution unmanned aerial vehicle SAR sparse imaging device with frequency band synthesis, characterized in that: include: A data acquisition module, used to acquire a first matrix of multiple groups of radar echo data; wherein the first matrix is ​​a full sampling matrix, and the frequency of each group of radar echo data is different; A first calculation module is used to perform sparse sampling on the first matrix of each group of radar echo data to obtain a second matrix corresponding to the first matrix; A second calculation module, used for calculating a third matrix of each group of radar echo data based on the first matrix of each group of radar echo data and a second matrix corresponding to the first matrix; A third calculation module is used to perform range migration correction on the third matrix of each group of radar echo data to obtain first target data of each group of radar echo data; A first synthesis module is used to perform bandwidth synthesis on the first target data of each group of radar echo data to obtain second target data; A first correction module, used for constructing an approximate observation operator based on a first matrix of each group of radar echo data, and imaging the second target data based on the approximate observation operator to obtain a pre-output image; The result imaging module is used to perform multiple residual phase error corrections on the pre-output image to obtain an imaging image.

9. A terminal device, comprising: A processor and a memory, wherein the memory stores a computer program that can be run on the processor, wherein when the processor executes the computer program, the high-resolution unmanned aerial vehicle SAR sparse imaging method with frequency band synthesis as described in any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the high-resolution unmanned aerial vehicle SAR sparse imaging method with frequency band synthesis as claimed in any one of claims 1 to 7 is implemented.

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